OncoAgent Framework for Privacy-Preserving Multi-Agent Oncology Clinical Decision Support

The OncoAgent framework utilizes a hierarchical dual-tier structure to coordinate multiple AI agents for oncological analysis. This design separates data processing from high-level reasoning, allowing the system to handle sensitive patient information securely. By distributing tasks among specialized agents, the platform can analyze diverse data sources such as genomic reports and clinical history without exposing raw data to unauthorized components. Privacy preservation is a core component of this implementation, addressing the strict regulatory requirements of the medical field. The system employs local processing and federated-style communication to ensure that patient-identifiable information remains protected during the decision-making process. This approach enables medical institutions to leverage large-scale AI models without compromising patient confidentiality or violating data residency laws. Engineers implementing this framework need to focus on the orchestration of these agents and the integration of specialized medical datasets. The architecture is designed to be extensible, supporting the addition of new diagnostic tools or specialized research modules as they become available. Developers should evaluate the computational overhead of the dual-tier communication to ensure performance meets clinical timing requirements and operational standards.